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The Dark Side of Machine Learning in Cybersecurity

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The Dark Side of Machine Learning in Cybersecurity


While machine learning (ML) is revolutionizing many industries including cybersecurity, it also has a dark side that often goes unaddressed.

With benefits come potential risks too. Who would have ever thought that hackers could use maskininlärning for hacking too? Well, looks like they can, and here’s how.

Due to its path-breaking innovation, ML has become one of the hottest buzzwords in the last few years. The ability to make machines learn from past experiences without being explicitly programmed helps ML gain everyone’s attention. ML has disrupted various industries, including healthcare, retail, and manufacturing, with its incredible features. However, every technology comes with its own set of drawbacks that experts should identify and resolve at the earliest. One of the significant applications of ML is in the field of cybersecurity. While on one handm, ML is helping overcome various security related vulnerabilities, on the other hand it is assisting hackers too. Read on to know how ML can defeat cyber threats and at the the same time how hackers can use ML for hacking.

How Can Machine Learning Overcome Cyber Frauds?

ML’s ability to learn in real-time helps companies to analyze any suspicious activity in their security network and resolve it proactively. With its self-learning feature, ML can learn from its past mistakes or past data breaches and predict similar vulnerabilities for future, helping companies to reduce the time they invest in remediation. Furthermore, ML can process large swarms of datasets – structured or unstructured – and detect anomalies in real-time, if any. Thanks to ML for making systems and software self-learn and for assisting humans in crucial decision-making. So, it’s certainly true that ML overcomes cyber frauds, but that it helps hackers as well, is a fact that cannot be denied too.

How Do Hackers Use Machine Learning For Hacking?

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It is quite ironic how hackers benefit themselves with ML, whereas the technology was aimed at detecting and protecting businesses from such threats. So, is there a secret friendship flourishing between ML and hackers that we are unaware of? Yes, there is.

  • Malware creation – Hackers invest a lot of their time to create the malware that is then pushed into organizational systems. They have to write long and complex codes to develop malware, such as adware, rootkit, bug, spyware, before injecting them into a system. ML can help hackers to create malware. One of the examples of ML helping create malware was presented in a report named “Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN,” which states how algorithms were built to develop malware that could easily find another path to security systems.
  • Phishing emails – ML has incredible applications, such as NLP, voice recognition, and text-to-speech, that help companies offer sophisticated ways to run their business. Hackers can use ML and its applications for writing a believable mail by training the ML system with legitimate mails and creating similar mails that could be sent to targets.
  • Destroying the ML system – ML gathers large masses of datasets and works on it. But what if the input data is poisoned? Will the ML system still work fine? No, it won’t. Hackers can destroy the input datasets on which the ML systems work to make the system corrupted.



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The Boring Technology Transformation


Is the word disruption over-used or overrated? Paul Saunders thinks so.

Paul is head of product strategy and chief evangelist on S/4HANA at SAP. Prior to SAP, Paul was a CIO at multiple manufacturing businesses, and an executive at a university and Gardner research. With a strong technology, manufacturing and global background, Paul offers unique and profound perspectives.

With all the supply chain, geopolitical and pandemic disruptions coming together, the smart way of doing business has not changed. It is about flexibility, agility, process, and mindset. Business, people, and technology often move at different rates. Still technology cannot be perceived as “long after I need it, and for way more money than I’m willing to pay”.

There are exciting technologies like metaverse and blockchain, but business flow does not change day to day from procuring supplies, providing goods and services, and getting paid. The goal of SAP cloud ERP transformation is to simplify and standardize processes that do not require differentiation, to reduce variability, and to drive operational efficiency. As companies expand their network of intelligent business enterprise, they can focus more on creating true differentiation to win the future. Paul’s message on cloud ERP transformation resonates with me.

I agree with Paul that technology is a journey not a destination. When asked about the future workforce, here is his prediction. People are not expected to have the same job for 20 years and retire with a clock. A person will be more likely enjoying a portfolio of careers. That is insightful and exciting.

For those who are interested in hearing more from Paul and on “how to outsmart inflation”, find out more here



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